// 神经网络模型定义


// 2. 蒙特卡洛树搜索(MCTS)实现
// 替换原有的Alpha-Beta搜索算法：
class MCTS {
  constructor(network) {
    this.network = network;
    this.Q = {}; // 动作价值
    this.N = {}; // 访问次数
    this.P = {}; // 策略概率
  }
  
  async search(state, simulations=800) {
    for (let i = 0; i < simulations; i++) {
      await this.simulate(state);
    }
    
    const visits = Object.entries(this.N[state.hash()])
      .map(([action, count]) => [action, count]);
    visits.sort((a, b) => b[1] - a[1]);
    
    return visits[0][0]; // 返回访问次数最多的动作
  }
  
  async simulate(state) {
    if (state.isTerminal()) {
      return -state.reward();
    }
    
    const stateHash = state.hash();
    if (!(stateHash in this.P)) {
      // 叶子节点扩展
      const inputTensor = state.toTensor();
      const {policy, value} = this.network.predict(inputTensor);
      this.P[stateHash] = policy.dataSync();
      this.Q[stateHash] = Array(policy.shape[1]).fill(0);
      this.N[stateHash] = Array(policy.shape[1]).fill(0);
      return -value.dataSync()[0];
    }
    
    // 选择动作
    const action = this.selectAction(stateHash);
    const nextState = state.nextState(action);
    const v = await this.simulate(nextState);
    
    // 回溯更新
    this.Q[stateHash][action] = (this.N[stateHash][action] * this.Q[stateHash][action] - v) / 
      (this.N[stateHash][action] + 1);
    this.N[stateHash][action] += 1;
    
    return v;
  }
  
  selectAction(stateHash) {
    const totalVisits = this.N[stateHash].reduce((a, b) => a + b);
    const c_puct = 1.0;
    
    let bestAction = -1;
    let bestValue = -Infinity;
    
    for (let action = 0; action < this.P[stateHash].length; action++) {
      const U = c_puct * this.P[stateHash][action] * 
        Math.sqrt(totalVisits) / (1 + this.N[stateHash][action]);
      const Q = this.Q[stateHash][action];
      const actionValue = Q + U;
      
      if (actionValue > bestValue) {
        bestValue = actionValue;
        bestAction = action;
      }
    }
    
    return bestAction;
  }
}

// 3. 状态表示和游戏逻辑
class ChessState {

  constructor(board, player, history=[]) {
    this.board = board; // 10x9的棋盘
    this.player = player; // 1(红方)或-1(黑方)
    this.history = history; // 历史状态
  }
  
  hash() {
    // 生成唯一状态标识
    return JSON.stringify({
      board: this.board,
      player: this.player,
      history: this.history.slice(-2) // 只考虑最近两步历史
    });
  }
  
  isTerminal() {
    // 检查游戏是否结束(将死或和棋)
    // return this.isCheckmate() || this.isDraw();
    return play.checkFoul() !== false;
  }
  
  reward() {
    // 终局奖励
    if (this.isCheckmate()) return this.player === 1 ? -1 : 1;
    return 0; // 和棋
  }
  
  toTensor() {
    // 将状态转换为神经网络输入张量
    const planes = [];
    
    // 棋子类型平面(6种×2方=12平面)
    for (let pieceType = 0; pieceType < 6; pieceType++) {
      for (let player = -1; player <= 1; player += 2) {
        const plane = Array(10).fill().map(() => Array(9).fill(0));
        for (let y = 0; y < 10; y++) {
          for (let x = 0; x < 9; x++) {
            const piece = this.board[y][x];
            if (piece && piece.type === pieceType && piece.player === player) {
              plane[y][x] = 1;
            }
          }
        }
        planes.push(plane);
      }
    }
    
    // 历史平面(最近2步)
    for (let i = 0; i < 2; i++) {
      const historyState = this.history[this.history.length - 1 - i];
      const plane = Array(10).fill().map(() => Array(9).fill(0));
      if (historyState) {
        for (let y = 0; y < 10; y++) {
          for (let x = 0; x < 9; x++) {
            if (historyState.board[y][x] !== this.board[y][x]) {
              plane[y][x] = 1;
            }
          }
        }
      }
      planes.push(plane);
    }
    
    return tf.tensor4d([planes]);
  }
  
  legalMoves() {
    // 生成所有合法走法(替换原有的getMoves函数)
    const moves = [];
    // ...实现走法生成逻辑...
    return moves;
  }
  
  nextState(move) {
    // 执行走法并返回新状态
    const newBoard = JSON.parse(JSON.stringify(this.board));
    // ...执行走法逻辑...
    return new ChessState(newBoard, -this.player, [...this.history, this]);
  }
}

// 4. 训练循环实现
async function train() {
  const network = new ChessNet();
  const optimizer = tf.train.adam(0.001);
  
  // 自我对弈生成数据
  async function selfPlay() {
    const examples = [];
    const mcts = new MCTS(network);
    initialBoard = com.initMap;
    let state = new ChessState(initialBoard, 1);
    
    while (!state.isTerminal()) {
      const action = await mcts.search(state);
      const policy = Array(2086).fill(0);
      policy[action] = 1;
      
      examples.push({
        state: state.toTensor(),
        policy: tf.tensor1d(policy),
        value: null
      });
      
      state = state.nextState(action);
    }
    
    // 设置终局奖励
    const reward = state.reward();
    examples.forEach(ex => {
      ex.value = tf.tensor1d([reward]);
      reward *= -1; // 交替奖励
    });
    
    return examples;
  }
  
  // 训练步骤
  async function trainStep(batch) {
    const states = tf.concat(batch.map(ex => ex.state));
    const policies = tf.concat(batch.map(ex => ex.policy));
    const values = tf.concat(batch.map(ex => ex.value));
    
    return optimizer.minimize(() => {
      const [predPolicies, predValues] = network.model.predict(states);
      const policyLoss = tf.losses.softmaxCrossEntropy(policies, predPolicies);
      const valueLoss = tf.losses.meanSquaredError(values, predValues);
      return tf.add(policyLoss, valueLoss);
    });
  }
  
  // 主训练循环
  const replayBuffer = [];
  for (let epoch = 0; epoch < 1000; epoch++) {
    const examples = await selfPlay();
    replayBuffer.push(...examples);
    if (replayBuffer.length > 5000) {
      replayBuffer.splice(0, replayBuffer.length - 5000);
    }
    
    // 随机采样批次
    const batch = [];
    for (let i = 0; i < 32; i++) {
      batch.push(replayBuffer[Math.floor(Math.random() * replayBuffer.length)]);
    }
    
    await trainStep(batch);
    
    // 定期评估和保存模型
    if (epoch % 50 === 0) {
      await network.model.save('downloads://chess-ai');
    }
  }
}

var rl = rl ||  {};
rl.init = function(){
    train();
}

// 5. 与原有代码的集成
// 最后，修改原有的AI.init函数以使用新的MCTS：
// AI.init = async function(pace) {
//   // 检查开局库(保留原有逻辑)
//   // ...
  
//   // 使用MCTS进行搜索
//   const network = await tf.loadLayersModel('path/to/model.json');
//   const mcts = new MCTS(network);
//   const state = new ChessState(play.map, play.my);
//   const bestMove = await mcts.search(state);
  
//   // 转换走法格式与原有系统兼容
//   return [bestMove.fromX, bestMove.fromY, bestMove.toX, bestMove.toY];
// };